Estimating Real Demand Using a Flipped Queueing Model: A Case of Shared Micro-Mobility Services
🤖 AI Summary
Spatial and temporal imbalances between supply and demand in shared micromobility lead to demand censoring—where observed demand is truncated—obscuring latent demand and hindering accurate demand forecasting and fleet optimization. To address this, we propose a novel inverse estimation framework grounded in the Flipped Queueing Model (FQM), which innovatively employs Generalized Vehicle Sojourn Time (GVST) as an observable proxy variable and formulates demand estimation as an inverse queueing problem. We develop two complementary estimators: a closed-form one-sided estimator for analytical tractability and real-time decision support, and a high-accuracy two-sided system-of-equations estimator for refined inference. Experiments on synthetic data and real-world dockless bike and e-scooter datasets demonstrate that our approach significantly outperforms baseline methods. The one-sided estimator achieves a favorable balance of interpretability and operational efficiency, while the two-sided estimator delivers superior accuracy, providing a rigorous foundation for fine-grained fleet rebalancing and resource allocation.
Application Category
📝 Abstract
The spatial-temporal imbalance between supply and demand in shared micro-mobility services often leads to observed demand being censored, resulting in incomplete records of the underlying real demand. This phenomenon undermines the reliability of the collected demand data and hampers downstream applications such as demand forecasting, fleet management, and micro-mobility planning. How to accurately estimate the real demand is challenging and has not been well explored in existing studies. In view of this, we contribute to real demand estimation for shared micro-mobility services by proposing an analytical method that rigorously derives the real demand under appropriate assumptions. Rather than directly modeling the intractable relationship between observed demand and real demand, we propose a novel random variable, Generalized Vehicle Survival Time (GVST), which is observable from trip records. The relationship between GVST and real demand is characterized by introducing a flipped queueing model (FQM) that captures the operational dynamics of shared micro-mobility services. Specifically, the distribution of GVST is derived within the FQM, which allows the real demand estimation problem to be transformed into an inverse queueing problem. We analytically derive the real demand in closed form using a one-sided estimation method, and solve the problem by a system of equations in a two-sided estimation method. We validate the proposed methods using synthetic data and conduct empirical analyses using real-world datasets from bike-sharing and shared e-scooter systems. The experimental results show that both the two-sided and one-sided methods outperform benchmark models. In particular, the one-sided approach provides a closed-form solution that delivers acceptable accuracy, constituting a practical rule of thumb for demand-related analytics and decision-making processes.
Problem
Research questions and friction points this paper is trying to address.
Estimating real demand in shared micro-mobility using flipped queueing model
Addressing censored observed demand from supply-demand imbalance issues
Transforming demand estimation into inverse queueing problem via GVST
Innovation
Methods, ideas, or system contributions that make the work stand out.
Flipped queueing model captures micro-mobility operational dynamics
Generalized Vehicle Survival Time transforms demand estimation problem
Closed-form real demand solution derived via inverse queueing approach
🔎 Similar Papers
No similar papers found.
